A 6M Digital Twin for Reservoirs

  • Tao Zhang

Student thesis: Doctoral Thesis

Abstract

Modeling and simulation of flow, transport and geo-mechanics in the subsurface porous media is an effective approach to help make decisions associated with the management of subsurface oil and gas reservoirs, as well as in other wide application areas including groundwater contamination and carbon sequestration. Accurate modeling and efficient, robust simulation have always been the main purposes of reservoir researches, and a 6M digital twin (multi-scale, multi-domain, multi-physics and multi-numerics numerical modeling and simulation of multi-component and multiphase fluid flow in porous media) is designed in this dissertation, using certain advanced models and algorithms equipped with pronounced features, to better digitally model and simulate the engineering processes and procedures in the physical reality of reservoirs and further control and optimize such processes and procedures. A comprehensive mathematical tool package is generated in the digital space, equipped with advanced models and algorithms regarding various numerical schemes including Navier-Stokes equations, LBM formulations and Darcy equation. Deep learning algorithms are incorporated in the digital space to accelerate certain time-consuming computations, for example, flash calculation and geological feature detection. A number of engineering processes are successfully reflected in the digital twin, in multiple simulation scales including in a single pore, in a porous media and in a pipeline/separator, to provide plenty of feedback to the physical entity of the industry. Finally, a complete digital twin with the 6M properties is concluded and certain promising extensions from our digital twin is organized.
Date of AwardFeb 2021
Original languageEnglish (US)
Awarding Institution
  • Physical Science and Engineering
SupervisorShuyu Sun (Supervisor)

Cite this

'